Final Project

final

FIFA

Analysis of EA Sports FIFA Data

Author

Nayan Jani

Published

August 25, 2022

library(tidyverse)
library(ggplot2)
library(stringr)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to:

read in multiple data sets, and describe the data set using both words and any supporting information (e.g., tables, etc)
tidy data (as needed, including sanity checks)
mutate variables as needed (including sanity checks)
join two or more data sets and analyze some aspect of the joined data

(be sure to only include the category tags for the data you use!)

Read in data

file_path <- "_data/_players/"

csv_file_names <- list.files(path="_data/_players/", pattern="players*")

csv_file_names

[1] "players_15.csv" "players_16.csv" "players_17.csv" "players_18.csv"
[5] "players_19.csv" "players_20.csv"

Briefly describe the data

Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

csv_file_names %>%
  purrr::map(function(file_name){ 
  assign(x = str_remove(file_name, ".csv"), 
         value = read_csv(paste0(file_path, file_name)),
         envir = .GlobalEnv)
})

[[1]]
# A tibble: 15,465 × 104
   sofifa_id player_url short…¹ long_…²   age dob        heigh…³ weigh…⁴ natio…⁵
       <dbl> <chr>      <chr>   <chr>   <dbl> <date>       <dbl>   <dbl> <chr>  
 1    158023 https://s… L. Mes… Lionel…    27 1987-06-24     169      67 Argent…
 2     20801 https://s… Cristi… Cristi…    29 1985-02-05     185      80 Portug…
 3      9014 https://s… A. Rob… Arjen …    30 1984-01-23     180      80 Nether…
 4     41236 https://s… Z. Ibr… Zlatan…    32 1981-10-03     195      95 Sweden 
 5    167495 https://s… M. Neu… Manuel…    28 1986-03-27     193      92 Germany
 6    176580 https://s… L. Suá… Luis A…    27 1987-01-24     181      81 Uruguay
 7    183277 https://s… E. Haz… Eden H…    23 1991-01-07     173      74 Belgium
 8      7826 https://s… R. van… Robin …    30 1983-08-06     187      71 Nether…
 9    121944 https://s… B. Sch… Bastia…    29 1984-08-01     183      79 Germany
10    156616 https://s… F. Rib… Franck…    31 1983-04-07     170      72 France 
# … with 15,455 more rows, 95 more variables: club <chr>, overall <dbl>,
#   potential <dbl>, value_eur <dbl>, wage_eur <dbl>, player_positions <chr>,
#   preferred_foot <chr>, international_reputation <dbl>, weak_foot <dbl>,
#   skill_moves <dbl>, work_rate <chr>, body_type <chr>, real_face <chr>,
#   release_clause_eur <lgl>, player_tags <chr>, team_position <chr>,
#   team_jersey_number <dbl>, loaned_from <chr>, joined <date>,
#   contract_valid_until <dbl>, nation_position <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

[[2]]
# A tibble: 14,881 × 104
   sofifa_id player_url short…¹ long_…²   age dob        heigh…³ weigh…⁴ natio…⁵
       <dbl> <chr>      <chr>   <chr>   <dbl> <date>       <dbl>   <dbl> <chr>  
 1    158023 https://s… L. Mes… Lionel…    28 1987-06-24     170      72 Argent…
 2     20801 https://s… Cristi… Cristi…    30 1985-02-05     185      80 Portug…
 3      9014 https://s… A. Rob… Arjen …    31 1984-01-23     180      80 Nether…
 4    167495 https://s… M. Neu… Manuel…    29 1986-03-27     193      92 Germany
 5    176580 https://s… L. Suá… Luis A…    28 1987-01-24     182      85 Uruguay
 6    183277 https://s… E. Haz… Eden H…    24 1991-01-07     173      74 Belgium
 7     41236 https://s… Z. Ibr… Zlatan…    33 1981-10-03     195      95 Sweden 
 8    190871 https://s… Neymar  Neymar…    23 1992-02-05     174      68 Brazil 
 9    164240 https://s… Thiago… Thiago…    30 1984-09-22     183      79 Brazil 
10    168542 https://s… David … David …    29 1986-01-08     170      67 Spain  
# … with 14,871 more rows, 95 more variables: club <chr>, overall <dbl>,
#   potential <dbl>, value_eur <dbl>, wage_eur <dbl>, player_positions <chr>,
#   preferred_foot <chr>, international_reputation <dbl>, weak_foot <dbl>,
#   skill_moves <dbl>, work_rate <chr>, body_type <chr>, real_face <chr>,
#   release_clause_eur <lgl>, player_tags <chr>, team_position <chr>,
#   team_jersey_number <dbl>, loaned_from <chr>, joined <date>,
#   contract_valid_until <dbl>, nation_position <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

[[3]]
# A tibble: 17,009 × 104
   sofifa_id player_url short…¹ long_…²   age dob        heigh…³ weigh…⁴ natio…⁵
       <dbl> <chr>      <chr>   <chr>   <dbl> <date>       <dbl>   <dbl> <chr>  
 1     20801 https://s… Cristi… Cristi…    31 1985-02-05     185      80 Portug…
 2    158023 https://s… L. Mes… Lionel…    29 1987-06-24     170      72 Argent…
 3    190871 https://s… Neymar  Neymar…    24 1992-02-05     174      68 Brazil 
 4    167495 https://s… M. Neu… Manuel…    30 1986-03-27     193      92 Germany
 5    176580 https://s… L. Suá… Luis A…    29 1987-01-24     182      85 Uruguay
 6    193080 https://s… De Gea  David …    25 1990-11-07     193      82 Spain  
 7     41236 https://s… Z. Ibr… Zlatan…    34 1981-10-03     195      95 Sweden 
 8    173731 https://s… G. Bale Gareth…    26 1989-07-16     183      74 Wales  
 9    183907 https://s… J. Boa… Jérôme…    27 1988-09-03     192      90 Germany
10    188545 https://s… R. Lew… Robert…    27 1988-08-21     185      79 Poland 
# … with 16,999 more rows, 95 more variables: club <chr>, overall <dbl>,
#   potential <dbl>, value_eur <dbl>, wage_eur <dbl>, player_positions <chr>,
#   preferred_foot <chr>, international_reputation <dbl>, weak_foot <dbl>,
#   skill_moves <dbl>, work_rate <chr>, body_type <chr>, real_face <chr>,
#   release_clause_eur <lgl>, player_tags <chr>, team_position <chr>,
#   team_jersey_number <dbl>, loaned_from <chr>, joined <date>,
#   contract_valid_until <dbl>, nation_position <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

[[4]]
# A tibble: 17,592 × 104
   sofifa_id player_url short…¹ long_…²   age dob        heigh…³ weigh…⁴ natio…⁵
       <dbl> <chr>      <chr>   <chr>   <dbl> <date>       <dbl>   <dbl> <chr>  
 1     20801 https://s… Cristi… Cristi…    32 1985-02-05     185      80 Portug…
 2    158023 https://s… L. Mes… Lionel…    30 1987-06-24     170      72 Argent…
 3    190871 https://s… Neymar  Neymar…    25 1992-02-05     175      68 Brazil 
 4    167495 https://s… M. Neu… Manuel…    31 1986-03-27     193      92 Germany
 5    176580 https://s… L. Suá… Luis A…    30 1987-01-24     182      86 Uruguay
 6    188545 https://s… R. Lew… Robert…    28 1988-08-21     185      79 Poland 
 7    193080 https://s… De Gea  David …    26 1990-11-07     193      76 Spain  
 8    183277 https://s… E. Haz… Eden H…    26 1991-01-07     173      76 Belgium
 9    155862 https://s… Sergio… Sergio…    31 1986-03-30     183      75 Spain  
10    167664 https://s… G. Hig… Gonzal…    29 1987-12-10     184      87 Argent…
# … with 17,582 more rows, 95 more variables: club <chr>, overall <dbl>,
#   potential <dbl>, value_eur <dbl>, wage_eur <dbl>, player_positions <chr>,
#   preferred_foot <chr>, international_reputation <dbl>, weak_foot <dbl>,
#   skill_moves <dbl>, work_rate <chr>, body_type <chr>, real_face <chr>,
#   release_clause_eur <dbl>, player_tags <chr>, team_position <chr>,
#   team_jersey_number <dbl>, loaned_from <chr>, joined <date>,
#   contract_valid_until <dbl>, nation_position <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

[[5]]
# A tibble: 17,770 × 104
   sofifa_id player_url short…¹ long_…²   age dob        heigh…³ weigh…⁴ natio…⁵
       <dbl> <chr>      <chr>   <chr>   <dbl> <date>       <dbl>   <dbl> <chr>  
 1     20801 https://s… Cristi… Cristi…    33 1985-02-05     187      83 Portug…
 2    158023 https://s… L. Mes… Lionel…    31 1987-06-24     170      72 Argent…
 3    190871 https://s… Neymar… Neymar…    26 1992-02-05     175      68 Brazil 
 4    193080 https://s… De Gea  David …    27 1990-11-07     193      76 Spain  
 5    192985 https://s… K. De … Kevin …    27 1991-06-28     181      70 Belgium
 6    155862 https://s… Sergio… Sergio…    32 1986-03-30     184      82 Spain  
 7    176580 https://s… L. Suá… Luis A…    31 1987-01-24     182      86 Uruguay
 8    177003 https://s… L. Mod… Luka M…    32 1985-09-09     172      66 Croatia
 9    183277 https://s… E. Haz… Eden H…    27 1991-01-07     173      74 Belgium
10    200389 https://s… J. Obl… Jan Ob…    25 1993-01-07     188      87 Sloven…
# … with 17,760 more rows, 95 more variables: club <chr>, overall <dbl>,
#   potential <dbl>, value_eur <dbl>, wage_eur <dbl>, player_positions <chr>,
#   preferred_foot <chr>, international_reputation <dbl>, weak_foot <dbl>,
#   skill_moves <dbl>, work_rate <chr>, body_type <chr>, real_face <chr>,
#   release_clause_eur <dbl>, player_tags <chr>, team_position <chr>,
#   team_jersey_number <dbl>, loaned_from <chr>, joined <date>,
#   contract_valid_until <dbl>, nation_position <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

[[6]]
# A tibble: 18,278 × 104
   sofifa_id player_url short…¹ long_…²   age dob        heigh…³ weigh…⁴ natio…⁵
       <dbl> <chr>      <chr>   <chr>   <dbl> <date>       <dbl>   <dbl> <chr>  
 1    158023 https://s… L. Mes… Lionel…    32 1987-06-24     170      72 Argent…
 2     20801 https://s… Cristi… Cristi…    34 1985-02-05     187      83 Portug…
 3    190871 https://s… Neymar… Neymar…    27 1992-02-05     175      68 Brazil 
 4    200389 https://s… J. Obl… Jan Ob…    26 1993-01-07     188      87 Sloven…
 5    183277 https://s… E. Haz… Eden H…    28 1991-01-07     175      74 Belgium
 6    192985 https://s… K. De … Kevin …    28 1991-06-28     181      70 Belgium
 7    192448 https://s… M. ter… Marc-A…    27 1992-04-30     187      85 Germany
 8    203376 https://s… V. van… Virgil…    27 1991-07-08     193      92 Nether…
 9    177003 https://s… L. Mod… Luka M…    33 1985-09-09     172      66 Croatia
10    209331 https://s… M. Sal… Mohame…    27 1992-06-15     175      71 Egypt  
# … with 18,268 more rows, 95 more variables: club <chr>, overall <dbl>,
#   potential <dbl>, value_eur <dbl>, wage_eur <dbl>, player_positions <chr>,
#   preferred_foot <chr>, international_reputation <dbl>, weak_foot <dbl>,
#   skill_moves <dbl>, work_rate <chr>, body_type <chr>, real_face <chr>,
#   release_clause_eur <dbl>, player_tags <chr>, team_position <chr>,
#   team_jersey_number <dbl>, loaned_from <chr>, joined <date>,
#   contract_valid_until <dbl>, nation_position <chr>, …
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

players_15 <- players_15 %>%
  mutate(fifa_type = "15") %>%
  select(-c(player_url, long_name, body_type, real_face,release_clause_eur,
            player_tags,team_position, team_jersey_number, loaned_from, nation_position,nation_jersey_number,
            player_traits,mentality_composure,ls,st,rs,lw,lf,cf,rf,rw,lam,cam,ram,lm,lcm,cm,rcm,rm,lwb,ldm,cdm,rdm,
            rwb,lb,lcb,cb,rcb,rb))

players_16 <-players_16 %>%
  mutate(fifa_type = "16") %>%
  select(-c(player_url, long_name, body_type, real_face,release_clause_eur,
            player_tags,team_position, team_jersey_number, loaned_from, nation_position,nation_jersey_number,
            player_traits,mentality_composure,ls,st,rs,lw,lf,cf,rf,rw,lam,cam,ram,lm,lcm,cm,rcm,rm,lwb,ldm,cdm,rdm,
            rwb,lb,lcb,cb,rcb,rb))

players_17<- players_17 %>%
  mutate(fifa_type = "17") %>%
  select(-c(player_url, long_name, body_type, real_face,release_clause_eur,
            player_tags,team_position, team_jersey_number, loaned_from, nation_position,nation_jersey_number,
            player_traits,mentality_composure,ls,st,rs,lw,lf,cf,rf,rw,lam,cam,ram,lm,lcm,cm,rcm,rm,lwb,ldm,cdm,rdm,
            rwb,lb,lcb,cb,rcb,rb))

players_18 <- players_18 %>%
  mutate(fifa_type = "18") %>%
  select(-c(player_url, long_name, body_type, real_face,release_clause_eur,
            player_tags,team_position, team_jersey_number, loaned_from, nation_position,nation_jersey_number,
            player_traits,mentality_composure,ls,st,rs,lw,lf,cf,rf,rw,lam,cam,ram,lm,lcm,cm,rcm,rm,lwb,ldm,cdm,rdm,
            rwb,lb,lcb,cb,rcb,rb))

players_19<- players_19 %>%
  mutate(fifa_type = "19") %>%
  select(-c(player_url, long_name, body_type, real_face,release_clause_eur,
            player_tags,team_position, team_jersey_number, loaned_from, nation_position,nation_jersey_number,
            player_traits,mentality_composure,ls,st,rs,lw,lf,cf,rf,rw,lam,cam,ram,lm,lcm,cm,rcm,rm,lwb,ldm,cdm,rdm,
            rwb,lb,lcb,cb,rcb,rb))

players_20<- players_20 %>%
  mutate(fifa_type = "20") %>%
  select(-c(player_url, long_name, body_type, real_face,release_clause_eur,
            player_tags,team_position, team_jersey_number, loaned_from, nation_position,nation_jersey_number,
            player_traits,mentality_composure,ls,st,rs,lw,lf,cf,rf,rw,lam,cam,ram,lm,lcm,cm,rcm,rm,lwb,ldm,cdm,rdm,
            rwb,lb,lcb,cb,rcb,rb))

Join Data

Be sure to include a sanity check, and double-check that case count is correct!

players_15

players_16

players_17

players_18

players_19

players_20

vars_mut<- players_15%>%
  select(-c(sofifa_id, short_name, age, dob, height_cm, weight_kg, nationality, club , overall,
            potential, value_eur, wage_eur, player_positions, preferred_foot, international_reputation
            ,weak_foot,skill_moves, work_rate, joined ,contract_valid_until, pace, shooting, passing
            ,dribbling, defending, physic, gk_diving, gk_handling, gk_kicking ,gk_reflexes,
            gk_speed,gk_positioning, fifa_type)) 


col_var_mut <- colnames(vars_mut)




#players_15

sub.var <- function(x, na.rm=FALSE) (str_sub(x,start = 1, end = 2))

players_15_tidy<- players_15%>%
  mutate_at(col_var_mut, sub.var) %>%
  mutate_at(col_var_mut, as.double)
  
  
  
players_16_tidy <- players_16%>%
  mutate_at(col_var_mut, sub.var) %>%
  mutate_at(col_var_mut, as.double)

players_17_tidy <- players_17%>%
  mutate_at(col_var_mut, sub.var) %>%
  mutate_at(col_var_mut, as.double)

players_18_tidy <- players_18%>%
  mutate_at(col_var_mut, sub.var) %>%
  mutate_at(col_var_mut, as.double)

players_19_tidy <- players_19%>%
  mutate_at(col_var_mut, sub.var) %>%
  mutate_at(col_var_mut, as.double)

here i scraped the characters off all stats.

#players_15_tidy
#players_16_tidy
#players_17_tidy
#players_18_tidy
#players_19_tidy
#players_20

FIFA<- full_join(players_15_tidy,players_16_tidy) %>%
  full_join(players_17_tidy) %>%
  full_join(players_18_tidy) %>%
  full_join(players_19_tidy) %>%
  full_join(players_20)

# FIFA

sub.var1 <- function(x, na.rm=FALSE) (str_sub(x,start = 1, end = 3))

vis1<- FIFA %>%
  select(fifa_type,player_positions, height_cm) %>%
  group_by(fifa_type, player_positions) %>%
  mutate_at("player_positions",sub.var1)%>%
  summarise(avg = mean(height_cm)) %>%
  mutate(player_positions = str_replace(player_positions, "[:punct:]", "+"))
  
vis1

ggplot(vis1, aes(x=fifa_type, y=avg, group=player_positions, color =player_positions )) +
  geom_point(size=1) +
  geom_line() +
  theme(legend.position = "right") +
  labs(title = "Average Height of all postions Over Years",
     x = "Year of FIFA", y = "Average Height") + 
  theme_bw()

FIFA_tidy<- FIFA %>%
  pivot_longer(col =-c(sofifa_id, short_name, age, dob, height_cm, weight_kg, nationality, club , overall,
            potential, value_eur, wage_eur, player_positions, preferred_foot, international_reputation
            ,weak_foot,skill_moves, work_rate, joined ,contract_valid_until, fifa_type) , names_to = "stat",
            values_to = "value", values_drop_na = TRUE )


#FIFA_tidy

possible ideas:

Historical comparison between Messi and Ronaldo (what skill attributes changed the most during time - compared to real-life stats);

Ideal budget to create a competitive team (at the level of top n teams in Europe) and at which point the budget does not allow to buy significantly better players for the 11-men lineup. An extra is the same comparison with the Potential attribute for the lineup instead of the Overall attribute;

Sample analysis of top n% players (e.g. top 5% of the player) to see if some important attributes as Agility or BallControl or Strength have been popular or not acroos the FIFA versions. An example would be seeing that the top 5% players of FIFA 20 are more fast (higher Acceleration and Agility) compared to FIFA 15. The trend of attributes is also an important indication of how some attributes are necessary for players to win games (a version with more top 5% players with high BallControl stats would indicate that the game is more focused on the technique rather than the physicial aspect).

height of cbs over the years

FIFA_tidy %>%
  select(fifa_type,player_positions, height_cm) %>%
  filter(player_positions == "CB")